Mobile communication has developed extensively in the past few decades in terms of the diversity of applications, data rates, and the heterogeneity of device types. This growth requires constant technological advances to meet the increasing quality of service and capacity demand. Increasing capacity is a very challenging task due to the scarcity of the available electromagnetic spectrum and the aggressive frequency reuse; i.e., the same frequencies are used concurrently by adjacent Base Stations (BSs). This creates interference between BSs and User Equipment devices (UEs) in adjacent cells, interference which is a major factor in limiting overall network throughput. Thus, different levels of coordination/cooperation among BSs, known as Coordinated Multi-point (CoMP) techniques, are a key to enhancing the network capacity and to keeping interference at an adequate level to ensure service availability.
In downlink CoMP, BSs coordinate transmission to mitigate interference to each other's terminals or work as one to become a single distributed BS which serves all the UEs in a range by applying joint precoding and decoding. Two types of cooperation are Coordinated Scheduling and Coordinated Beamforming (CS/CB) and Joint Processing (JP), which increases the number of degrees of freedom. These schemes require joint optimization of BSs' precoding matrices. In general they require distribution of Channel State Information (CSI); i.e., the interference channel between each BS and UE. To reduce complexity and CSI acquisition overhead. BSs may cooperate only within clusters and treat out-of-cluster interference (OCI) as noise.
UEs estimate direct and interference channels from each BS. They then quantize the estimated channels according to codebooks designed for this purpose and send the quantized estimates as feedback to the BSs.
Previous work on CSI feedback includes work on codebook design and bit allocation based on channel types (i.e., direct channel or interference channel), as well as work on optimizing other network parameters, such as bit rate. Such work includes Wild, “A rake-finger based efficient channel state information feedback compression scheme for the MIMO OFDM FDD downlink,” İ Vehicular Technology Conference (VTC 2010-Spring), 2010 IEEE 71st, May 2010, whose disclosure is incorporated herein by reference. Efficient CSI codebooks can exploit temporal correlation and/or allocate a different number of bits to direct-channel feedback and interference-channel feedback, while taking channel gain into account. A further approach has been to allocate feedback bits to maximize the sum-rate. Other methods combine quantization with precoding design while taking into account very little feedback, or determine suitable transmission rank and scheduling with limited feedback. Furthermore, the feedback overhead can be reduced by excluding users with low channel gains from feeding back their CSI. Another technique for precoding design without feedback or with very little feedback is to apply distributed iterative procedures, where in each iteration the BSs adjust their precoding matrices based on local measurements, such as the overall interference at each UE. In general, iterative schemes provide good performance in slow varying channels, but are less suitable for moderate/high Doppler spread. Despite the progress in reducing CSI feedback overhead, or compensating for limited feedback, this constraint is still a major factor in limiting CoMP performance, and is recognized as an important challenge in the development of 5G networks.
Methods for handling inter-cell interference are described in U.S. Pat. No. 7,912,014 to Molisch, et al., whose disclosure is incorporated herein by reference. Additional methods are described in Gesbert, et al., “Multi-Cell MIMO Cooperative Networks: A New Look at Interference” (IEEE Journal on Selected areas of Communications, Vol. 28, No. 9, December 2010), and in Kerret, et al., “Spatial CSIT allocation policies for network MIMO channels,” (IEEE Transactions on Information Theory, vol. 60, pp. 4158-4169, July 2014), whose disclosures are incorporated herein by reference. Formulas for interference channels are presented in Wu, et al., “Degrees of Freedom of the Interference Channel: a General Formula”, 2011 IEEE International Symposium on Information Theory Proceedings, 1344-1348, whose disclosure is incorporated herein by reference. Notation: Matrices and vectors are denoted by boldface symbols, where italicized boldface letters, e.g. x, denote column vectors; whereas non-italicized boldface letters, e.g. x, denote row vectors, and bold capital letters denote matrices. ( )T and ( )† denote the transpose and conjugate transpose operations, respectively. IN is the N×N identity matrix, Tr(·) (denotes the trace of a matrix and ∥·∥ denotes the Frobenius norm.
Notation: Matrices and vectors are denoted by boldface symbols, where italicized boldface letters, e.g. x, denote column vector; whereas non-italicized boldface letters, e.g. x, denote row vector, and bold capital letters denote matrices. The conjugate transpose of matrix or vector is denoted by ( )*, IN is the N×N identity matrix, tr(·) denotes the trace of a matrix and ∥·∥ denotes the Frobenius norm.